Management of dental disease in eared seals (family Otariidae): a Delphi approach
Bibliographic record
Abstract
Objective: Published literature is sparse on topics associated with eared seal (otariid) dentistry. The objective of this study was to establish consensus on effective management of dental disease in otariids, using a Delphi approach. Methods: A total of 25 veterinarians with experience managing dental disease in seven species of otariids participated in the Delphi process. Results: Oral lesions and their contributing risk factors were ranked according to perceived frequency. Consensus statements for best practices were agreed upon for a variety of topics within the categories of planning and preparation, procedural details, intervention strategies, and postoperative care. Panelist comments were collated into a Supplementary File to assist clinicians in forming their own conclusions on topics for which no consensus yet exists. Conclusion: Opportunities for future research include factors associated with oral lesions, ideal anesthetic management, identification of ideal candidates for endodontic therapies, ideal local and regional anesthesia, ideal suture and closure techniques, particularly with the goal of reducing dehiscence as a postoperative complication, and ideal postoperative care options.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.060 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".